Overview

Dataset statistics

Number of variables12
Number of observations244
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.3 KiB
Average record size in memory215.3 B

Variable types

Numeric10
Categorical1
Text1

Alerts

code is highly overall correlated with elderly_alone_ratio and 1 other fieldsHigh correlation
latitude is highly overall correlated with provinceHigh correlation
longitude is highly overall correlated with provinceHigh correlation
elementary_school_count is highly overall correlated with kindergarten_count and 6 other fieldsHigh correlation
kindergarten_count is highly overall correlated with elementary_school_count and 6 other fieldsHigh correlation
university_count is highly overall correlated with elementary_school_count and 3 other fieldsHigh correlation
academy_ratio is highly overall correlated with elementary_school_count and 4 other fieldsHigh correlation
elderly_population_ratio is highly overall correlated with elementary_school_count and 4 other fieldsHigh correlation
elderly_alone_ratio is highly overall correlated with code and 5 other fieldsHigh correlation
nursing_home_count is highly overall correlated with elementary_school_count and 6 other fieldsHigh correlation
province is highly overall correlated with code and 6 other fieldsHigh correlation
code has unique valuesUnique
university_count has 92 (37.7%) zerosZeros

Reproduction

Analysis started2023-07-23 15:51:03.646352
Analysis finished2023-07-23 15:51:15.383183
Duration11.74 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

code
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct244
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32912.09
Minimum10000
Maximum80000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:15.469855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10121.5
Q114027.5
median30075
Q351062.5
95-th percentile61078.5
Maximum80000
Range70000
Interquartile range (IQR)37035

Descriptive statistics

Standard deviation19373.35
Coefficient of variation (CV)0.5886393
Kurtosis-1.4569427
Mean32912.09
Median Absolute Deviation (MAD)18950
Skewness0.2679525
Sum8030550
Variance3.7532668 × 108
MonotonicityStrictly increasing
2023-07-23T17:51:15.756199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 1
 
0.4%
41080 1
 
0.4%
41100 1
 
0.4%
41110 1
 
0.4%
41120 1
 
0.4%
41130 1
 
0.4%
41140 1
 
0.4%
41150 1
 
0.4%
50000 1
 
0.4%
50010 1
 
0.4%
Other values (234) 234
95.9%
ValueCountFrequency (%)
10000 1
0.4%
10010 1
0.4%
10020 1
0.4%
10030 1
0.4%
10040 1
0.4%
10050 1
0.4%
10060 1
0.4%
10070 1
0.4%
10080 1
0.4%
10090 1
0.4%
ValueCountFrequency (%)
80000 1
0.4%
70000 1
0.4%
61180 1
0.4%
61170 1
0.4%
61160 1
0.4%
61150 1
0.4%
61140 1
0.4%
61130 1
0.4%
61120 1
0.4%
61110 1
0.4%

province
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size16.3 KiB
Gyeonggi-do
32 
Seoul
26 
Gyeongsangbuk-do
24 
Jeollanam-do
23 
Gyeongsangnam-do
19 
Other values (13)
120 

Length

Max length17
Median length12
Mean length10.782787
Min length5

Characters and Unicode

Total characters2631
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.2%

Sample

1st rowSeoul
2nd rowSeoul
3rd rowSeoul
4th rowSeoul
5th rowSeoul

Common Values

ValueCountFrequency (%)
Gyeonggi-do 32
13.1%
Seoul 26
10.7%
Gyeongsangbuk-do 24
9.8%
Jeollanam-do 23
9.4%
Gyeongsangnam-do 19
7.8%
Gangwon-do 19
7.8%
Busan 17
7.0%
Chungcheongnam-do 16
 
6.6%
Jeollabuk-do 15
 
6.1%
Chungcheongbuk-do 12
 
4.9%
Other values (8) 41
16.8%

Length

2023-07-23T17:51:15.885491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gyeonggi-do 32
13.1%
seoul 26
10.7%
gyeongsangbuk-do 24
9.8%
jeollanam-do 23
9.4%
gyeongsangnam-do 19
7.8%
gangwon-do 19
7.8%
busan 17
7.0%
chungcheongnam-do 16
 
6.6%
jeollabuk-do 15
 
6.1%
chungcheongbuk-do 12
 
4.9%
Other values (8) 41
16.8%

Most occurring characters

ValueCountFrequency (%)
o 366
13.9%
n 328
12.5%
g 241
 
9.2%
a 203
 
7.7%
e 202
 
7.7%
- 161
 
6.1%
d 161
 
6.1%
u 138
 
5.2%
l 108
 
4.1%
G 100
 
3.8%
Other values (19) 623
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2226
84.6%
Uppercase Letter 244
 
9.3%
Dash Punctuation 161
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 366
16.4%
n 328
14.7%
g 241
10.8%
a 203
9.1%
e 202
9.1%
d 161
7.2%
u 138
 
6.2%
l 108
 
4.9%
y 75
 
3.4%
h 67
 
3.0%
Other values (9) 337
15.1%
Uppercase Letter
ValueCountFrequency (%)
G 100
41.0%
J 39
 
16.0%
C 28
 
11.5%
S 27
 
11.1%
B 17
 
7.0%
D 15
 
6.1%
I 11
 
4.5%
U 6
 
2.5%
K 1
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2470
93.9%
Common 161
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 366
14.8%
n 328
13.3%
g 241
9.8%
a 203
 
8.2%
e 202
 
8.2%
d 161
 
6.5%
u 138
 
5.6%
l 108
 
4.4%
G 100
 
4.0%
y 75
 
3.0%
Other values (18) 548
22.2%
Common
ValueCountFrequency (%)
- 161
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 366
13.9%
n 328
12.5%
g 241
 
9.2%
a 203
 
7.7%
e 202
 
7.7%
- 161
 
6.1%
d 161
 
6.1%
u 138
 
5.2%
l 108
 
4.1%
G 100
 
3.8%
Other values (19) 623
23.7%

city
Text

Distinct222
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
2023-07-23T17:51:16.137738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.9467213
Min length5

Characters and Unicode

Total characters2427
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)88.1%

Sample

1st rowSeoul
2nd rowGangnam-gu
3rd rowGangdong-gu
4th rowGangbuk-gu
5th rowGangseo-gu
ValueCountFrequency (%)
dong-gu 6
 
2.5%
jung-gu 6
 
2.5%
seo-gu 5
 
2.0%
nam-gu 4
 
1.6%
buk-gu 4
 
1.6%
gangseo-gu 2
 
0.8%
goseong-gun 2
 
0.8%
guro-gu 1
 
0.4%
dongdaemun-gu 1
 
0.4%
dobong-gu 1
 
0.4%
Other values (212) 212
86.9%
2023-07-23T17:51:16.505026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 352
14.5%
g 318
13.1%
u 241
9.9%
- 235
9.7%
o 195
 
8.0%
e 156
 
6.4%
a 143
 
5.9%
s 119
 
4.9%
i 105
 
4.3%
h 57
 
2.3%
Other values (31) 506
20.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1948
80.3%
Uppercase Letter 244
 
10.1%
Dash Punctuation 235
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 352
18.1%
g 318
16.3%
u 241
12.4%
o 195
10.0%
e 156
8.0%
a 143
7.3%
s 119
 
6.1%
i 105
 
5.4%
h 57
 
2.9%
y 38
 
2.0%
Other values (11) 224
11.5%
Uppercase Letter
ValueCountFrequency (%)
G 51
20.9%
S 30
12.3%
Y 26
10.7%
J 23
9.4%
D 20
 
8.2%
B 14
 
5.7%
H 14
 
5.7%
C 13
 
5.3%
N 11
 
4.5%
U 8
 
3.3%
Other values (9) 34
13.9%
Dash Punctuation
ValueCountFrequency (%)
- 235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2192
90.3%
Common 235
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 352
16.1%
g 318
14.5%
u 241
11.0%
o 195
8.9%
e 156
 
7.1%
a 143
 
6.5%
s 119
 
5.4%
i 105
 
4.8%
h 57
 
2.6%
G 51
 
2.3%
Other values (30) 455
20.8%
Common
ValueCountFrequency (%)
- 235
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 352
14.5%
g 318
13.1%
u 241
9.9%
- 235
9.7%
o 195
 
8.0%
e 156
 
6.4%
a 143
 
5.9%
s 119
 
4.9%
i 105
 
4.3%
h 57
 
2.3%
Other values (31) 506
20.8%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct243
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.396996
Minimum33.488936
Maximum38.380571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:16.644256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum33.488936
5-th percentile34.834183
Q135.405263
median36.386601
Q337.466119
95-th percentile37.82439
Maximum38.380571
Range4.891635
Interquartile range (IQR)2.060856

Descriptive statistics

Standard deviation1.0603044
Coefficient of variation (CV)0.029131647
Kurtosis-1.2322596
Mean36.396996
Median Absolute Deviation (MAD)1.0649305
Skewness-0.1205314
Sum8880.867
Variance1.1242455
MonotonicityNot monotonic
2023-07-23T17:51:16.779661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.566953 2
 
0.8%
35.015818 1
 
0.4%
36.080266 1
 
0.4%
36.789844 1
 
0.4%
36.680222 1
 
0.4%
36.81498 1
 
0.4%
36.459129 1
 
0.4%
36.745577 1
 
0.4%
36.60126 1
 
0.4%
35.820308 1
 
0.4%
Other values (233) 233
95.5%
ValueCountFrequency (%)
33.488936 1
0.4%
34.310983 1
0.4%
34.486834 1
0.4%
34.573374 1
0.4%
34.61117 1
0.4%
34.642006 1
0.4%
34.681616 1
0.4%
34.760421 1
0.4%
34.771453 1
0.4%
34.800192 1
0.4%
ValueCountFrequency (%)
38.380571 1
0.4%
38.207022 1
0.4%
38.146693 1
0.4%
38.110002 1
0.4%
38.106152 1
0.4%
38.096409 1
0.4%
38.075405 1
0.4%
38.069682 1
0.4%
37.903568 1
0.4%
37.894881 1
0.4%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct243
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.6614
Minimum126.26355
Maximum130.90588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:16.906579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum126.26355
5-th percentile126.61403
Q1126.92766
median127.38425
Q3128.47395
95-th percentile129.1651
Maximum130.90588
Range4.642329
Interquartile range (IQR)1.5462897

Descriptive statistics

Standard deviation0.90478127
Coefficient of variation (CV)0.0070873519
Kurtosis-0.63971418
Mean127.6614
Median Absolute Deviation (MAD)0.6000745
Skewness0.61714614
Sum31149.382
Variance0.81862914
MonotonicityNot monotonic
2023-07-23T17:51:17.048059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.977977 2
 
0.8%
126.710826 1
 
0.4%
126.691394 1
 
0.4%
127.00242 1
 
0.4%
126.844687 1
 
0.4%
127.113868 1
 
0.4%
126.802333 1
 
0.4%
126.29795 1
 
0.4%
126.660772 1
 
0.4%
127.108791 1
 
0.4%
Other values (233) 233
95.5%
ValueCountFrequency (%)
126.263554 1
0.4%
126.29795 1
0.4%
126.351636 1
0.4%
126.392198 1
0.4%
126.450289 1
0.4%
126.463021 1
0.4%
126.481653 1
0.4%
126.487777 1
0.4%
126.500423 1
0.4%
126.512087 1
0.4%
ValueCountFrequency (%)
130.905883 1
0.4%
129.416575 1
0.4%
129.400585 1
0.4%
129.366107 1
0.4%
129.36124 1
0.4%
129.343645 1
0.4%
129.332773 1
0.4%
129.330047 1
0.4%
129.311538 1
0.4%
129.242507 1
0.4%

elementary_school_count
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.180328
Minimum4
Maximum6087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:17.190997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.15
Q114.75
median22
Q336.25
95-th percentile217.9
Maximum6087
Range6083
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation402.71348
Coefficient of variation (CV)5.4288447
Kurtosis206.95551
Mean74.180328
Median Absolute Deviation (MAD)9
Skewness13.948602
Sum18100
Variance162178.15
MonotonicityNot monotonic
2023-07-23T17:51:17.352587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 16
 
6.6%
21 14
 
5.7%
16 12
 
4.9%
14 10
 
4.1%
22 8
 
3.3%
17 8
 
3.3%
23 8
 
3.3%
11 7
 
2.9%
12 7
 
2.9%
19 7
 
2.9%
Other values (68) 147
60.2%
ValueCountFrequency (%)
4 4
 
1.6%
5 1
 
0.4%
6 2
 
0.8%
7 2
 
0.8%
8 4
 
1.6%
9 2
 
0.8%
10 6
 
2.5%
11 7
2.9%
12 7
2.9%
13 16
6.6%
ValueCountFrequency (%)
6087 1
0.4%
1277 1
0.4%
607 1
0.4%
501 1
0.4%
471 1
0.4%
429 1
0.4%
419 1
0.4%
409 1
0.4%
349 1
0.4%
304 1
0.4%

kindergarten_count
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.90164
Minimum4
Maximum8837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:17.520845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q116
median31
Q355.25
95-th percentile325.6
Maximum8837
Range8833
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation588.78832
Coefficient of variation (CV)5.4567134
Kurtosis201.48458
Mean107.90164
Median Absolute Deviation (MAD)16
Skewness13.737066
Sum26328
Variance346671.69
MonotonicityNot monotonic
2023-07-23T17:51:17.681230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 13
 
5.3%
12 9
 
3.7%
16 9
 
3.7%
14 7
 
2.9%
43 6
 
2.5%
8 6
 
2.5%
26 6
 
2.5%
17 6
 
2.5%
18 5
 
2.0%
37 5
 
2.0%
Other values (91) 172
70.5%
ValueCountFrequency (%)
4 1
 
0.4%
5 1
 
0.4%
6 1
 
0.4%
7 1
 
0.4%
8 6
2.5%
9 5
2.0%
10 3
 
1.2%
11 5
2.0%
12 9
3.7%
13 5
2.0%
ValueCountFrequency (%)
8837 1
0.4%
2237 1
0.4%
830 1
0.4%
707 1
0.4%
686 1
0.4%
542 1
0.4%
519 1
0.4%
499 1
0.4%
408 1
0.4%
403 1
0.4%

university_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1516393
Minimum0
Maximum340
Zeros92
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:17.831503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile14.4
Maximum340
Range340
Interquartile range (IQR)3

Descriptive statistics

Standard deviation22.513041
Coefficient of variation (CV)5.422687
Kurtosis206.12168
Mean4.1516393
Median Absolute Deviation (MAD)1
Skewness13.889369
Sum1013
Variance506.83699
MonotonicityNot monotonic
2023-07-23T17:51:17.948227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 92
37.7%
1 50
20.5%
2 34
 
13.9%
3 20
 
8.2%
4 18
 
7.4%
6 7
 
2.9%
5 4
 
1.6%
7 3
 
1.2%
17 2
 
0.8%
19 2
 
0.8%
Other values (11) 12
 
4.9%
ValueCountFrequency (%)
0 92
37.7%
1 50
20.5%
2 34
 
13.9%
3 20
 
8.2%
4 18
 
7.4%
5 4
 
1.6%
6 7
 
2.9%
7 3
 
1.2%
8 1
 
0.4%
10 1
 
0.4%
ValueCountFrequency (%)
340 1
0.4%
61 1
0.4%
48 1
0.4%
33 1
0.4%
22 1
0.4%
21 2
0.8%
19 2
0.8%
18 1
0.4%
17 2
0.8%
15 1
0.4%

academy_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2947541
Minimum0.19
Maximum4.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:18.091805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.5015
Q10.87
median1.27
Q31.6125
95-th percentile2.2525
Maximum4.18
Range3.99
Interquartile range (IQR)0.7425

Descriptive statistics

Standard deviation0.5928979
Coefficient of variation (CV)0.45792317
Kurtosis3.5968454
Mean1.2947541
Median Absolute Deviation (MAD)0.38
Skewness1.2090602
Sum315.92
Variance0.35152792
MonotonicityNot monotonic
2023-07-23T17:51:18.275905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.39 8
 
3.3%
1.16 5
 
2.0%
0.67 5
 
2.0%
0.83 5
 
2.0%
1.03 4
 
1.6%
1.01 4
 
1.6%
1.09 4
 
1.6%
1.34 4
 
1.6%
0.78 3
 
1.2%
1.35 3
 
1.2%
Other values (134) 199
81.6%
ValueCountFrequency (%)
0.19 1
0.4%
0.2 1
0.4%
0.21 1
0.4%
0.25 1
0.4%
0.28 1
0.4%
0.32 1
0.4%
0.35 1
0.4%
0.36 1
0.4%
0.37 1
0.4%
0.39 1
0.4%
ValueCountFrequency (%)
4.18 1
0.4%
4.03 1
0.4%
3.23 1
0.4%
3.02 1
0.4%
2.88 1
0.4%
2.63 1
0.4%
2.6 1
0.4%
2.5 1
0.4%
2.49 1
0.4%
2.38 1
0.4%

elderly_population_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct229
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.92373
Minimum7.69
Maximum40.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:18.422397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.69
5-th percentile10.432
Q114.1175
median18.53
Q327.2625
95-th percentile35.396
Maximum40.26
Range32.57
Interquartile range (IQR)13.145

Descriptive statistics

Standard deviation8.0874279
Coefficient of variation (CV)0.38651943
Kurtosis-0.83564554
Mean20.92373
Median Absolute Deviation (MAD)5.575
Skewness0.54588335
Sum5105.39
Variance65.40649
MonotonicityNot monotonic
2023-07-23T17:51:18.548810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.52 3
 
1.2%
30.27 2
 
0.8%
17 2
 
0.8%
32.97 2
 
0.8%
30.89 2
 
0.8%
30.17 2
 
0.8%
12.82 2
 
0.8%
12.65 2
 
0.8%
12.88 2
 
0.8%
12.77 2
 
0.8%
Other values (219) 223
91.4%
ValueCountFrequency (%)
7.69 1
0.4%
8.58 1
0.4%
8.86 1
0.4%
9.04 1
0.4%
9.08 1
0.4%
9.09 1
0.4%
9.1 1
0.4%
9.48 2
0.8%
10.02 1
0.4%
10.22 1
0.4%
ValueCountFrequency (%)
40.26 1
0.4%
40.04 1
0.4%
38.87 1
0.4%
38.44 1
0.4%
37.43 1
0.4%
36.92 1
0.4%
36.55 1
0.4%
36.45 1
0.4%
36.36 1
0.4%
36.08 1
0.4%

elderly_alone_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.644672
Minimum3.3
Maximum24.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:18.682744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile4.3
Q16.1
median8.75
Q314.625
95-th percentile21.525
Maximum24.7
Range21.4
Interquartile range (IQR)8.525

Descriptive statistics

Standard deviation5.6048858
Coefficient of variation (CV)0.52654377
Kurtosis-0.55220079
Mean10.644672
Median Absolute Deviation (MAD)3.45
Skewness0.76775254
Sum2597.3
Variance31.414745
MonotonicityNot monotonic
2023-07-23T17:51:18.812257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.8 6
 
2.5%
6.7 6
 
2.5%
6.5 5
 
2.0%
5.2 5
 
2.0%
8.8 4
 
1.6%
6.9 4
 
1.6%
6.8 4
 
1.6%
11.1 4
 
1.6%
5.4 4
 
1.6%
7.4 3
 
1.2%
Other values (120) 199
81.6%
ValueCountFrequency (%)
3.3 2
0.8%
3.4 1
 
0.4%
3.6 1
 
0.4%
3.8 3
1.2%
3.9 1
 
0.4%
4 2
0.8%
4.1 2
0.8%
4.3 2
0.8%
4.4 2
0.8%
4.5 2
0.8%
ValueCountFrequency (%)
24.7 1
0.4%
24.5 1
0.4%
24.2 1
0.4%
23.8 1
0.4%
23.7 1
0.4%
23.3 1
0.4%
22.5 1
0.4%
22.3 1
0.4%
22 2
0.8%
21.7 1
0.4%

nursing_home_count
Real number (ℝ)

HIGH CORRELATION 

Distinct208
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.2582
Minimum11
Maximum94865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-23T17:51:18.941782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile53
Q1111
median300
Q3694.5
95-th percentile2964.2
Maximum94865
Range94854
Interquartile range (IQR)583.5

Descriptive statistics

Standard deviation6384.1851
Coefficient of variation (CV)5.5071296
Kurtosis193.64951
Mean1159.2582
Median Absolute Deviation (MAD)219.5
Skewness13.400691
Sum282859
Variance40757819
MonotonicityNot monotonic
2023-07-23T17:51:19.070125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191 3
 
1.2%
94 3
 
1.2%
62 3
 
1.2%
186 3
 
1.2%
41 2
 
0.8%
147 2
 
0.8%
108 2
 
0.8%
317 2
 
0.8%
80 2
 
0.8%
96 2
 
0.8%
Other values (198) 220
90.2%
ValueCountFrequency (%)
11 1
0.4%
24 1
0.4%
32 1
0.4%
33 1
0.4%
36 1
0.4%
37 1
0.4%
41 2
0.8%
44 1
0.4%
46 1
0.4%
47 1
0.4%
ValueCountFrequency (%)
94865 1
0.4%
22739 1
0.4%
20491 1
0.4%
6752 1
0.4%
5364 1
0.4%
5083 1
0.4%
4497 1
0.4%
4474 1
0.4%
3774 1
0.4%
3641 1
0.4%

Interactions

2023-07-23T17:51:13.957390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:03.946630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.975608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:06.096458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.224179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:08.276129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:09.552556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:10.603824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:11.760036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:12.852525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:14.056415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.080077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:05.112203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:06.233418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.349868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:08.381859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:09.660171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:10.707031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:11.865103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:12.958348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:14.169707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.172805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:05.208274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:06.351707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.450161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:08.483711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:09.783324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:10.802161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:11.960435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:13.071877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:14.288197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.270149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:05.301549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:06.469047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.576228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:08.580415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:09.881901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:10.916903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:12.083001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:13.185493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:14.416278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.367018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:05.401092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:06.595669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.672462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:08.701672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:09.978898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:11.043646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:12.209671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:13.278401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:14.532371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.455184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:05.496637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:06.687095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.765847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:08.953246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:10.071404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:11.170586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:12.300144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:13.361640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:14.638592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.558768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:05.602847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:06.801212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.871357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:09.085507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:10.168031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:11.312710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:12.404287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:13.489106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:14.739902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.668555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:05.743015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:06.914182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.973944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:09.220070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:10.278018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:11.433177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:12.541648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:13.624344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:14.838794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.777277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:05.866650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.022491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:08.080477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:09.347213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:10.382765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:11.542927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:12.671657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:13.747752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:14.933054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:04.877416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:05.982343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:07.125427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:08.181165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:09.456270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:10.487592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:11.659895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:12.761345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-23T17:51:13.874770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-23T17:51:19.172600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
codelatitudelongitudeelementary_school_countkindergarten_countuniversity_countacademy_ratioelderly_population_ratioelderly_alone_rationursing_home_countprovince
code1.000-0.3950.119-0.142-0.198-0.162-0.2080.4970.596-0.4840.979
latitude-0.3951.000-0.1910.0690.0680.049-0.076-0.271-0.3690.1460.656
longitude0.119-0.1911.000-0.181-0.173-0.0900.0060.1160.145-0.1610.522
elementary_school_count-0.1420.069-0.1811.0000.9670.6820.642-0.616-0.5710.8180.669
kindergarten_count-0.1980.068-0.1730.9671.0000.6890.685-0.697-0.6510.8560.669
university_count-0.1620.049-0.0900.6820.6891.0000.483-0.467-0.4560.6660.669
academy_ratio-0.208-0.0760.0060.6420.6850.4831.000-0.707-0.6420.6890.244
elderly_population_ratio0.497-0.2710.116-0.616-0.697-0.467-0.7071.0000.974-0.7270.265
elderly_alone_ratio0.596-0.3690.145-0.571-0.651-0.456-0.6420.9741.000-0.7330.296
nursing_home_count-0.4840.146-0.1610.8180.8560.6660.689-0.727-0.7331.0000.669
province0.9790.6560.5220.6690.6690.6690.2440.2650.2960.6691.000

Missing values

2023-07-23T17:51:15.088157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-23T17:51:15.281313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

codeprovincecitylatitudelongitudeelementary_school_countkindergarten_countuniversity_countacademy_ratioelderly_population_ratioelderly_alone_rationursing_home_count
010000SeoulSeoul37.566953126.977977607830481.4415.385.822739
110010SeoulGangnam-gu37.518421127.047222333804.1813.174.33088
210020SeoulGangdong-gu37.530492127.123837273201.5414.555.41023
310030SeoulGangbuk-gu37.639938127.025508142100.6719.498.5628
410040SeoulGangseo-gu37.551166126.849506365611.1714.395.71080
510050SeoulGwanak-gu37.478290126.951502223310.8915.124.9909
610060SeoulGwangjin-gu37.538712127.082366223331.1613.754.8723
710070SeoulGuro-gu37.495632126.887650263431.0016.215.7741
810080SeoulGeumcheon-gu37.456852126.895229181900.9616.156.7475
910090SeoulNowon-gu37.654259127.056294426661.3915.407.4952
codeprovincecitylatitudelongitudeelementary_school_countkindergarten_countuniversity_countacademy_ratioelderly_population_ratioelderly_alone_rationursing_home_count
23461110Gyeongsangnam-doJinju-si35.180313128.108750455362.4916.278.6597
23561120Gyeongsangnam-doChangnyeong-gun35.544603128.492330172000.8029.8018.4129
23661130Gyeongsangnam-doChangwon-si35.227992128.68181511019551.8413.646.51701
23761140Gyeongsangnam-doTongyeong-si34.854426128.433210202901.7018.479.8230
23861150Gyeongsangnam-doHadong-gun35.067224127.751271161500.8432.8919.194
23961160Gyeongsangnam-doHaman-gun35.272481128.406540162001.1923.7414.794
24061170Gyeongsangnam-doHamyang-gun35.520541127.725177131201.0132.6520.983
24161180Gyeongsangnam-doHapcheon-gun35.566702128.165870171500.7138.4424.796
24270000Jeju-doJeju-do33.488936126.50042311312341.5315.106.41245
24380000KoreaKorea37.566953126.977977608788373401.5615.677.294865